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JAKUBÍČEK, R.; CHMELÍK, J.; OUŘEDNÍČEK, P.; JAN, J.
Original Title
Deep-learning-based fully automatic spine centerline detection in CT data
English Title
Type
Paper in proceedings (conference paper)
Original Abstract
In this contribution, we present a fully automatic approach, that is based on two convolution neural networks (CNN) together with a spine tracing algorithm utilizing a population optimization algorithm. Based on the evaluation of 130 CT scans including heavily distorted and complicated cases, it turned out that this new combination enables fast and robust detection with almost 90% of correctly determined spinal centerlines with computing time of fewer than 20 seconds.
English abstract
Keywords
CT; spine centerline; machine learning
Key words in English
Authors
RIV year
2020
Released
07.10.2019
Publisher
IEEE
Location
Berlin, Germany
ISBN
978-1-5386-1312-2
Book
2019 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
1557-170X
Periodical
Proceedings IEEE EMBC
Volume
19
State
United States of America
Pages from
2407
Pages to
2410
Pages count
4
URL
https://ieeexplore.ieee.org/document/8856528
BibTex
@inproceedings{BUT157840, author="Roman {Jakubíček} and Jiří {Chmelík} and Petr {Ouředníček} and Jiří {Jan}", title="Deep-learning-based fully automatic spine centerline detection in CT data", booktitle="2019 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)", year="2019", journal="Proceedings IEEE EMBC", volume="19", number="19", pages="2407--2410", publisher="IEEE", address="Berlin, Germany", doi="10.1109/EMBC.2019.8856528", isbn="978-1-5386-1312-2", issn="1557-170X", url="https://ieeexplore.ieee.org/document/8856528" }
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